Telecommunication Information Technology (TIT)
Research on DDOS Attack Detection Method Based on CNN-Transformer Combination Model
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- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
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- 간행물
- The International Journal of Advanced Smart Convergence 바로가기
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- 통권
- Volume 14 Number 3 (2025.09)바로가기
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- 페이지
- pp.1-15
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A474308
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- In recent years, Distributed Denial of Service (DDoS) attacks have become increasingly frequent, posing serious threats to the security and stability of network systems. To enhance the effectiveness of DDoS detection, this paper proposes a deep learning model that integrates Convolutional Neural Networks (CNN) with a Transformer architecture to achieve efficient recognition of multiple types of attacks on the CIC-DDoS2019 dataset. By combining feature extraction and temporal modeling, the model fully captures both spatial and contextual information in network traffic, significantly improving detection accuracy and robustness. Experimental results demonstrate that the proposed method outperforms traditional CNN-based models across several sub-datasets, achieving higher accuracy, recall, and F1 scores while maintaining a favorable balance between training and inference time. This research offers new insights and technical support for developing efficient and scalable intelligent network defense systems.
목차
Abstract
1. Introduction
1.1 Research Background
1.2 Research Objective
1.3 Organization of the Paper
2. Related Work
2.1 Overview of DDoS Attacks
2.2 Evolution of DDoS Detection: From Machine Learning to Deep Learning
2.3 Representative Deep Learning-Based DDoS Detection Studies
2.4 Transformer-CNN Combination Models with Attention Mechanisms and Innovations of This Study
3. The Proposed Method
3.1 Dataset Description
3.2 Model Architecture
3.3 Model Training
3.4 Performance Evaluation
4. Experiments and Results Analysis
4.1 Training Environment and Parameter Settings
4.2 Experimental Results
5. Conclusion and Future Work
5.1 Conclusion
5.2 Limitations
5.3 Future Research Directions
References
키워드
DDoS Attack Detection Deep Learning Convolutional Neural Network Transformer Network Traffic Analysis저자
간행물 정보
발행기관
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- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
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- 간행물명
- The International Journal of Advanced Smart Convergence
- 간기
- 계간
- pISSN
- 2288-2847
- eISSN
- 2288-2855
- 수록기간
- 2012~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / The International Journal of Advanced Smart Convergence Volume 14 Number 3
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